AI Eats the World? A Reality Check with Benedict Evans

**Benedict Evans** (0:00)
Mobile didn't need to wait for the Internet. The Internet didn't need to wait for PCs, and PCs didn't need to wait for consumer electronics and semiconductors and so on. So you've always got this accelerating adoption.

**SPEAKER_2** (0:10)
Benedict Evans is a tech analyst known for his presentation, AI eats the world. He sees AI differently than the world, spotting patterns others miss and dives into how people really use AI.

**Benedict Evans** (0:23)
They built this amazing piece, incredibly sophisticated, very expensive global infrastructure, with enormous growth in use all the time, and it changed all of our lives, and we all pay for it, and they didn't make any money from it, because all the value moved up the stack. The place that's got the product market fit right now is coding. Ben Swapit's gone from whatever it was, 9 billion run rate at the end of last year to 47 billion dollars run rate now. That's all software, isn't it? So what happens when someone else in some other field gets something worse? One of the characteristics of tech is that the moment that you understand something and you know what's going to happen is the moment you should move on to something else.

**Erik Torenberg** (0:55)
Google said that the risk of under-investing is riskier than over-investing.

**Benedict Evans** (0:58)
Investors are kind of looking at all these companies and saying...

**SPEAKER_2** (1:01)
Every major technology platform shift creates the same challenge, separating what we know from what we're guessing.
AI is already changing software development, reshaping infrastructure spending, and forcing companies to rethink products and workflows. But many of the biggest questions remain open. Who captures value? What becomes a product? What gets automated? And what entirely new categories emerge?
Benedict Evans has spent years studying how previous technology waves unfolded, from PCs and the internet, to smartphones and cloud computing. In this conversation, we discuss what AI has already changed, what remains uncertain, and how to think about the next phase of the AI transition.

**Erik Torenberg** (1:48)
Benedict, welcome back to the A16z Podcast.

**SPEAKER_2** (1:51)
Thank you.

**Erik Torenberg** (1:52)
Last time you were here, we were discussing the first iteration of your presentation, AI Eats the World. You wrote it almost a year and a half ago. At this point, you always begin your presentation with, what are the big questions? But I'm curious this time, before getting to the questions going forward, I want you to reflect on what have we learned since you originally made the presentation? What's played out? And let's reflect back.

**Benedict Evans** (2:12)
What's changed in the last year? So I think we have much more of a sense of diverging product strategy.
We have much more of a sense of competitive tension that goes beyond just make a bigger model faster with more compute. We've had several iterations of OpenAI strategy in particular from sort of everything all at once yesterday to, oops, no, maybe we should double down on coding. Clearly, agentic coding started working, and so all the focus in tech has kind of narrowed in massively onto that as something that has an absolute product market fit in the sense that the customers are pulling it out of your hands. And of course, that comes with a supply crunch around capacity and price imbalance, imbalance of supply, demand, capacity, CAPEX pricing that we see at the moment. So that's kind of the big shift. We had a moment of this is kind of sort of working and kind of exciting, but we're not quite sure what we're all going to do with it until it works for coding.
We'll work for anything else, yes, almost certainly, but that's what's working right now. And so that's become, we've got this kind of much narrower focus. Otherwise, the Charm on Numbers keep coming up, the models keep getting bigger, the capex keeps growing, the usage keeps growing, people are using this more. But most of the sort of fundamental questions you might have had two or three years ago didn't really have answers. Like we don't know if there'll be a winner in the models. We don't know if they can capture value up the stack. We don't know how much the models can do. We don't see a way that consumers will use this daily rather than weekly with the technology we have right now. So all of those questions are still open.

**Erik Torenberg** (3:40)
Yeah, and on the coding, could we have foreseen that that would have been the use case that really would have taken off? What's your reflection on that?

**Benedict Evans** (3:48)
Deterministically, you could have said, well, look, who's messing about with this stuff? Software developers, what are software developers going to try and make work software development? So at a very kind of simplistic, naive level, well, yeah, the software should work. It's offered about first year software development, just as like kind of I often compare this moment to like the Internet in 97, 98 But it's also like the PCs in the early 80s or the late 70s. It's incredibly exciting. It's not quite clear what it's for, and it doesn't quite work yet. And clearly, the first thing that people did with PCs was make computers.